The invention relates generally to a method and system for processing medical image data to aid in the detection and diagnosis of disease. More particularly, the invention relates to a method and system for processing medical image data to perform patient specific analysis of disease relevant changes in diseases such as chronic obstructive pulmonary disease.
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death in the United States and other countries. COPD has two main disease processes, namely, tissue destruction (emphysema) and airway inflammation (chronic bronchitis). At present, there is no known treatment that can reverse the progress of the disease. At best, the progress of the disease can only be halted. Thus, there is a premium placed on its early disease diagnosis and treatment. With early diagnosis and effective treatment, a patient's quality of life can be improved.
COPD is identified based on symptoms including coughing, wheezing, and shortness of breath (dyspnea). COPD includes a number of respiratory diseases, the most prominent of which are emphysema and chronic bronchitis. COPD affects large airways, small airways and parenchyma in patients. Diseases are typically caused by smoking and air pollution, and are linked to genetic predisposition causing alpha-anti-elastase deficiency.
Emphysema, or airspace destruction, is the most prominent feature of parenchymal change in COPD patients. Emphysema is the result of the loss of elastic recoil of lung tissue. There are four types of emphysema: centrilobular, panlobular or panacinar, distal acinar or paraseptal, and irregular. The first two types contribute to the majority of emphysematous COPD. The classification is based on the anatomical distribution of airspace destruction within a lobule, which is a cluster of acini. Currently, emphysema can be classified only through post mortem examination. Emphysema is typically diagnosed by gross physiological responses, medical imaging and post mortem anatomical inspection.
An X-ray chest radiograph system is the more commonly used diagnostic tool for the purpose of detecting lung disease in humans. Lung diseases such as bronchitis, emphysema and lung cancer are also detectable in Computed Tomography (CT). However, CT systems generally provide over 80 separate images for a single CT scan thereby providing a considerable amount of information to a radiologist for use in interpreting the images and detecting suspect regions that may indicate disease.
The use of high resolution CT image data is a promising technique for diagnosing diseases of the lung. However, in diseases such as emphysema, it is difficult for a radiologist to classify the extent of disease progression by only looking at the CT images since one of the more prominent disease indicators of emphysema is degradation of the alveoli and other tissue changes of the lung, which are currently difficult to measure from CT image data.
Some known diagnosis techniques have attempted to use simple CT images to attempt to quantify emphysema. Some of these techniques include using feature-based analysis, CT metrics such as density masks, AMFM and fractal analysis. However, the above techniques are not based on an underlying model of the disease and the reliability of the results obtained with these techniques can be affected by variable scan parameters and scanner calibration as well as other disease pathologies. In addition, known techniques do not provide estimates of the rate or location of tissue destruction, are not based on “patient specific statistics”, and typically only provide evidence of the progression of the disease for a patient based on population statistics.
Therefore, there is a need for a method and system for measuring disease relevant tissue changes in medical images to enable the diagnosis and tracking of various forms of COPD. Also, what is needed is a method and system for performing “patient specific analysis” of disease relevant changes of diseases such as COPD.